Loading...
Loading...
Found 324 Skills
Full Sentry SDK setup for React Native and Expo. Use when asked to "add Sentry to React Native", "install @sentry/react-native", "setup Sentry in Expo", or configure error monitoring, tracing, profiling, session replay, or logging for React Native applications. Supports Expo managed, Expo bare, and vanilla React Native.
Full Sentry SDK setup for Apple platforms (iOS, macOS, tvOS, watchOS, visionOS). Use when asked to "add Sentry to iOS", "add Sentry to Swift", "install sentry-cocoa", or configure error monitoring, tracing, profiling, session replay, or logging for Apple applications. Supports SwiftUI and UIKit.
Full Sentry SDK setup for .NET. Use when asked to "add Sentry to .NET", "install Sentry for C#", or configure error monitoring, tracing, profiling, logging, or crons for ASP.NET Core, MAUI, WPF, WinForms, Blazor, Azure Functions, or any other .NET application.
Use this skill when working with SigNoz - open-source observability platform for application monitoring, distributed tracing, log management, metrics, alerts, and dashboards. Triggers on SigNoz setup, OpenTelemetry instrumentation for SigNoz, sending traces/logs/metrics to SigNoz, creating SigNoz dashboards, configuring SigNoz alerts, exception monitoring, and migrating from Datadog/Grafana/New Relic to SigNoz.
Expert knowledge for Azure SignalR Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing SignalR mode, configuring upstreams/custom domains, securing with Entra ID/MI, scaling/sharding, or tracing issues, and other Azure SignalR Service related development tasks. Not for Azure Web PubSub (use azure-web-pubsub), Azure Service Bus (use azure-service-bus), Azure Event Hubs (use azure-event-hubs).
AI-powered JavaScript reverse engineering tool. Senior JavaScript reverse engineering expert assistant. Actions: collect, search, deobfuscate, understand, summarize, detect-crypto, browser, debugger, breakpoint, debug-step, debug-eval, debug-vars, script, hook, stealth, dom, page. Capabilities: obfuscated code analysis, VM cracking, Webpack unpacking, AST transformation, Puppeteer/CDP automation, anti-detection, fingerprint spoofing, encryption identification, parameter extraction, algorithm restoration, Canvas/WebGL fingerprinting, WebDriver hiding, CDP debugging, breakpoint analysis, dynamic tracing, Hook injection, DOM inspection, page control.
Comprehensive codebase reading engine. Systematically reads actual source code line by line through a 6-phase protocol — scoping, structural mapping, execution tracing, deep reading, pattern synthesis, and structured reporting. Source code is the source of truth. Use when needing to truly understand how code works, not just what documentation claims.
Discovers business domains in a Swift codebase by tracing what users can DO — not by reading folder names or architecture docs. Maps each domain's vertical slice (Types → Config → Repo → Service → Runtime → UI), identifies providers (external SDK bridges), and separates cross-cutting concerns. Produces a domain map that drives all downstream decisions: folder structure, SPM targets, enforcement specs, migration plans. Use this skill whenever the user wants to understand their codebase domains, find what's cross-cutting vs domain-specific, restructure a Swift project, figure out where code belongs, or map a product's capabilities to architectural boundaries. Triggers on "what are my domains", "where does this belong", "map this codebase", "what's cross-cutting", "organize this project", "is this a domain or infra", "restructure this", "architecture review", or any request to understand the business domain structure of a Swift codebase.
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
This skill should be used when the user asks for a cryptographer, cryptography review, help to choose a cipher (AES-GCM, ChaCha20-Poly1305, ECDH, RSA tradeoffs), key management, PKI design, TLS configuration, protocol security or handshake review, authenticated encryption, digital signature scheme design, post-quantum migration at architecture level, ProVerif or Tamarin modeling concepts, nonce reuse or IV misuse analysis, HKDF vs password hashing (Argon2), HSM or KMS usage patterns, secure randomness, side-channel and constant-time requirements, or cryptographic agility and algorithm deprecation—not general OWASP web app review only (information-security-engineer), secure coding checklists without crypto depth, Solidity or smart contract audits, blockchain wallet tracing, legal export classification, or shipping custom production crypto without design and review gates.
Unity shaders, materials, and rendering pipelines (URP/HDRP/Built-in). PROACTIVELY activate for: (1) writing shaders in Shader Graph, HLSL, or ShaderLab, (2) URP and HDRP shader authoring, (3) custom render pipeline work (SRP), (4) lighting setup (baked vs realtime, lightmaps, Global Illumination), (5) post-processing stacks, (6) reflection probes and light probes, (7) custom render features and full-screen passes, (8) shader stripping and variant management, (9) compute shaders, (10) ray tracing in HDRP. Provides: Shader Graph templates, HLSL snippets, URP/HDRP differences, lighting setup recipes, render-feature examples, and shader-variant guidance.
Reverse Paper Reading Method: Given a paper, recursively identify the previous papers it critiques and improves on (max 5 layers), then find the latest research progress published after it, and tell the evolution history of the relevant problem forward from the source. Centered on problems, explain the problems identified by each paper and their solution innovations in a Feynman-style manner. Use when user shares a paper and wants to understand its intellectual lineage, citation chain, problem evolution, or says 'reverse reading', 'paper traceability', 'paper context', 'paper river', 'paper connects', 'trace back', 'the ins and outs of this paper', 'paper evolution'. Also trigger when user wants to understand how a research problem evolved across multiple papers.